Pain Perception in Patients Treated with Ligating/Self-Ligating Brackets versus Patients Treated with Aligners
Bibliographic record
Abstract
This study compared the perception of pain experienced by patients undergoing orthodontic treatment with conventional, self-ligating brackets and aligners, and investigated the impact that pain had on their daily lives. 346 consecutive patients were included in the study: 115 patients treated with conventional brackets, 112 Patients treated with self-ligating brackets, and 119 patients treated with aligners. The quantitative aspect of pain was assessed using the Visual Analogue Scale, while the qualitative aspect of pain was evaluated using the Moroccan Short Form of McGILL Pain questionnaire. In all three groups experienced pain after activation tended to decrease in the following week. This pain was greater in patients with conventional braces and less in patients with aligners. Using the M-SF-MPQ to describe the qualitative aspect of the pain revealed that the “cramping مزير,” “aching تيألم ” aspect was most accentuated in the 3 groups. Medication intake was correlated with the intensity of pain experienced in all 3 systems. As for the impact of pain on daily activities, patients in groups of conventional and self-ligating braces showed more pain than those in the aligners group. Overall, aligners were less painful than conventional and self-ligating appliances. Patients did not suffer from an alteration in their quality of life due to orthodontic treatment.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".